Statin Toxicity From Macrolide Antibiotic Coprescription
Bibliographic record
Abstract
BACKGROUND: Clarithromycin and erythromycin, but not azithromycin, inhibit cytochrome P450 isoenzyme 3A4 (CYP3A4), and inhibition increases blood concentrations of statins that are metabolized by CYP3A4. OBJECTIVE: To measure the frequency of statin toxicity after coprescription of a statin with clarithromycin or erythromycin. DESIGN: Population-based cohort study. SETTING: Ontario, Canada, from 2003 to 2010. PATIENTS: Continuous statin users older than 65 years who were prescribed clarithromycin (n = 72,591) or erythromycin (n = 3267) compared with those prescribed azithromycin (n = 68,478). MEASUREMENTS: The primary outcome was hospitalization with rhabdomyolysis within 30 days of the antibiotic prescription. RESULTS: Atorvastatin was the most commonly prescribed statin (73%) followed by simvastatin and lovastatin. Compared with azithromycin, coprescription of a statin with clarithromycin or erythromycin was associated with a higher risk for hospitalization with rhabdomyolysis (absolute risk increase, 0.02% [95% CI, 0.01% to 0.03%]; relative risk [RR], 2.17 [CI, 1.04 to 4.53]) or with acute kidney injury (absolute risk increase, 1.26% [CI, 0.58% to 1.95%]; RR, 1.78 [CI, 1.49 to 2.14]) and for all-cause mortality (absolute risk increase, 0.25% [CI, 0.17% to 0.33%]; RR, 1.56 [CI, 1.36 to 1.80]). LIMITATIONS: Only older adults were included in the study. The absolute risk increase for rhabdomyolysis may be underestimated because the codes used to identify it were insensitive. CONCLUSION: In older adults, coprescription of clarithromycin or erythromycin with a statin that is metabolized by CYP3A4 increases the risk for statin toxicity. PRIMARY FUNDING SOURCE: Academic Medical Organization of Southwestern Ontario.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".